Transfer learning has two major approaches: the first approach is to pretrain and then fine-tune. Fine-tuning is a common practice in training neural networks. As it is difficult to collect large datasets for specific problems, researchers often train their networks first on ImageNet, a large-scale object classification dataset that contains 1.2 million images with 1000 classes [1]}. ImageNet-pretrained models are also available in deep learning libraries. Fine-tuning may be done several times: [2]} introduced cascaded fine-tuning, where the researchers first trained a deep CNN model on ImageNet dataset, an auxiliary dataset related to emotion recognition, and finally on the target dataset.
